Machine Learning Based MTBI Classification Using Diffusion MRI

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February 14, 2018, at 03:01 PM EST by 172.16.16.54 -
Changed lines 28-32 from:
  • Shervin Minaee, Siyun Wang, Yao Wang, Sohae Chung, Xiuyuan Wang, Els Fieremans, Steven Flanagan, Joseph Rath, Yvonne W Lui, "Identifying Mild Traumatic Brain Injury Patients From MR Images Using Bag of Visual Words,", IEEE Signal Processing in Medicine and Biology Symposium, 2017.
  • Shervin Minaee, Yao Wang, Sohae Chung, Xiuyuan Wang, Els Fieremans, Steven Flanagan, Joseph Rath, Yvonne W Lui, "A Machine Learning Approach For Identifying Patients with Mild Traumatic Brain Injury Using Diffusion MRI Modeling", ASFNR 11th Annual Meeting, 2017.
  • S Minaee, Y Wang, YW Lui, "Prediction of longterm outcome of neuropsychological tests of MTBI patients using imaging features," Signal Processing in Medicine and Biology Symposium (SPMB), IEEE, 2013.
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  • Shervin Minaee, Yao Wang, Anna Choromanska, Sohae Chung, Xiuyuan Wang, Els Fieremans, Steven Flanagan, Joseph Rath, Yvonne W Lui, "A Deep Unsupervised Learning Approach Toward MTBI Identification Using Diffusion MRI", International Engineering in Medicine and Biology Conference, IEEE, 2018 (submitted).
  • Shervin Minaee, Siyun Wang, Yao Wang, Sohae Chung, Xiuyuan Wang, Els Fieremans, Steven Flanagan, Joseph Rath, Yvonne W Lui, "Identifying Mild Traumatic Brain Injury Patients From MR Images Using Bag of Visual Words,", IEEE Signal Processing in Medicine and Biology Symposium, Dec 2017.
  • Shervin Minaee, Yao Wang, Sohae Chung, Xiuyuan Wang, Els Fieremans, Steven Flanagan, Joseph Rath, Yvonne W Lui, "A Machine Learning Approach For Identifying Patients with Mild Traumatic Brain Injury Using Diffusion MRI Modeling", ASFNR 11th Annual Meeting, Oct 2017.
  • S Minaee, Y Wang, YW Lui, "Prediction of longterm outcome of neuropsychological tests of MTBI patients using imaging features," Signal Processing in Medicine and Biology Symposium (SPMB), IEEE, Dec 2013.
February 14, 2018, at 02:58 PM EST by 172.16.16.54 -
Changed line 28 from:
  • Shervin Minaee, Siyun Wang, Yao Wang, Sohae Chung, Xiuyuan Wang, Els Fieremans, Steven Flanagan, Joseph Rath, Yvonne W Lui, "Identifying Mild Traumatic Brain Injury Patients From MR Images Using Bag of Visual Words, IEEE Signal Processing in Medicine and Biology Symposium, 2017.
to:
  • Shervin Minaee, Siyun Wang, Yao Wang, Sohae Chung, Xiuyuan Wang, Els Fieremans, Steven Flanagan, Joseph Rath, Yvonne W Lui, "Identifying Mild Traumatic Brain Injury Patients From MR Images Using Bag of Visual Words,", IEEE Signal Processing in Medicine and Biology Symposium, 2017.
February 14, 2018, at 02:57 PM EST by 172.16.16.54 -
Changed line 30 from:
  • Shervin Minaee, Yao Wang, Sohae Chung, Xiuyuan Wang, Els Fieremans, Steven Flanagan, Joseph Rath, Yvonne W Lui, "A Machine Learning Approach For Identifying Patients with Mild Traumatic Brain Injury Using Diffusion MRI Modeling, ASFNR 11th Annual Meeting, 2017.
to:
  • Shervin Minaee, Yao Wang, Sohae Chung, Xiuyuan Wang, Els Fieremans, Steven Flanagan, Joseph Rath, Yvonne W Lui, "A Machine Learning Approach For Identifying Patients with Mild Traumatic Brain Injury Using Diffusion MRI Modeling", ASFNR 11th Annual Meeting, 2017.
February 14, 2018, at 02:56 PM EST by 172.16.16.54 -
Added lines 27-30:
  • Shervin Minaee, Siyun Wang, Yao Wang, Sohae Chung, Xiuyuan Wang, Els Fieremans, Steven Flanagan, Joseph Rath, Yvonne W Lui, "Identifying Mild Traumatic Brain Injury Patients From MR Images Using Bag of Visual Words, IEEE Signal Processing in Medicine and Biology Symposium, 2017.
  • Shervin Minaee, Yao Wang, Sohae Chung, Xiuyuan Wang, Els Fieremans, Steven Flanagan, Joseph Rath, Yvonne W Lui, "A Machine Learning Approach For Identifying Patients with Mild Traumatic Brain Injury Using Diffusion MRI Modeling, ASFNR 11th Annual Meeting, 2017.
December 07, 2016, at 10:44 AM EST by 72.80.125.209 -
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This work aims to use features extracted from MR images taken shortly after injury to predict the performance of MTBI patients on neuropsychological tests one year after injury. Successful prediction can enable early patient stratification and proper treatment planning

to:

As another goal, this work aims to use features extracted from MR images taken shortly after injury to predict the performance of MTBI patients on neuropsychological tests one year after injury. Successful prediction can enable early patient stratification and proper treatment planning.

December 07, 2016, at 10:43 AM EST by 72.80.125.209 -
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This work aims to use features extracted from MR images taken shortly after injury to predict the performance of MTBI patients on neuropsychological tests one year after injury. Successful prediction can enable early patient stratification and proper treatment planning

December 07, 2016, at 10:42 AM EST by 72.80.125.209 -
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Mild Traumatic Brain Injury (MTBI) is a growing public health problem with an underestimated incidence of over one million people annually in the U.S. Neuropsychologicaltestsare used to both assess the patient condition and to monitor patient progress. This work aims to use features extracted from MRI images taken shortly after injury to predict whether a person has MTBI or not.

to:

Mild Traumatic Brain Injury (MTBI) is a growing public health problem with an underestimated incidence of over one million people annually in the U.S. Neuropsychological tests are used to both assess the patient condition and to monitor patient progress. This work aims to use features extracted from MRI images taken shortly after injury to predict whether a person has MTBI or not.

December 06, 2016, at 10:35 PM EST by 72.80.125.209 -
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Related Publications:

  • S Minaee, Y Wang, YW Lui, "Prediction of longterm outcome of neuropsychological tests of MTBI patients using imaging features," Signal Processing in Medicine and Biology Symposium (SPMB), IEEE, 2013.
December 06, 2016, at 10:26 PM EST by 72.80.125.209 -
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Prof. Yao Wang, Professor
Dr. Yvonne Lui, Professor

to:

Shervin Minaee, PhD candidate
Hugh Wang, Researcher
Prof. Yao Wang
Dr. Yvonne Lui

December 06, 2016, at 10:24 PM EST by 72.80.125.209 -
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The goal of this project is to determine the most effective features and corresponding prediction method for such prediction. The main challenge is that we have only limited training data, from which we need to develop the prediction method that can be expected to provide accurate prediction results for unseen data.

to:


Within this task, we need to also determine the most effective features and corresponding classification method. The main challenge is that we have only limited training data, from which we need to develop the prediction method that can be expected to provide accurate prediction results for unseen data.
As a long term goal, we also plan to use data augmentation technique to enlarge the number of samples, and exploit deep learning approach to perform classification directly from MRI images.

December 06, 2016, at 10:21 PM EST by 72.80.125.209 -
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Mild Traumatic Brain Injury (MTBI) is a growing public health problem with an underestimated incidence of over one million people annually in the U.S. Neuropsychologicaltestsare used to both assess the patient condition and to monitor patient progress. This work aims to use features extracted from MRI images taken shortly after injury to predict the performance of MTBI patients on neuropsychological tests one year after injury. The goal of this project is to determine the most effective features and corresponding prediction method for such prediction. The main challenge is that we have only limited training data, from which we need to develop the prediction method that can be expected to provide accurate prediction results for unseen data.

to:

Mild Traumatic Brain Injury (MTBI) is a growing public health problem with an underestimated incidence of over one million people annually in the U.S. Neuropsychologicaltestsare used to both assess the patient condition and to monitor patient progress. This work aims to use features extracted from MRI images taken shortly after injury to predict whether a person has MTBI or not.
The goal of this project is to determine the most effective features and corresponding prediction method for such prediction. The main challenge is that we have only limited training data, from which we need to develop the prediction method that can be expected to provide accurate prediction results for unseen data.

December 06, 2016, at 10:20 PM EST by 72.80.125.209 -
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The goal of this project is to determine the most effective features and corresponding prediction method for such prediction. The main challenge is that we have only limited training data, from which we need to develop the prediction method that can be expected to provide accurate prediction results for unseen data.

December 06, 2016, at 10:19 PM EST by 72.80.125.209 -
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This material is based upon work supported by the National Institute of Health

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This work is supported by the National Institute of Health

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Yao Wang, Professor
Mei Fu, Professor
An-Ti Chiang, Ph.D student
Qingyuan Liu, Master student

to:

Prof. Yao Wang, Professor
Dr. Yvonne Lui, Professor

December 06, 2016, at 10:18 PM EST by 72.80.125.209 -
Changed lines 7-20 from:

Mild Traumatic Brain Injury (MTBI) is a growing public health problem with an underestimated incidence of over one million people annually in the U.S. Neuropsychologicaltestsare used to both assess the patient condition and to monitor patient progress. This work aims to use features extracted from MRI images taken shortly after injury to predict the performance of MTBI patients on neuropsychological tests one year after injury.

to:

Mild Traumatic Brain Injury (MTBI) is a growing public health problem with an underestimated incidence of over one million people annually in the U.S. Neuropsychologicaltestsare used to both assess the patient condition and to monitor patient progress. This work aims to use features extracted from MRI images taken shortly after injury to predict the performance of MTBI patients on neuropsychological tests one year after injury.


Sponsor:
This material is based upon work supported by the National Institute of Health


Current Participants:
Yao Wang, Professor
Mei Fu, Professor
An-Ti Chiang, Ph.D student
Qingyuan Liu, Master student

December 06, 2016, at 10:17 PM EST by 72.80.125.209 -
December 06, 2016, at 10:16 PM EST by 72.80.125.209 -
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Page last modified on February 14, 2018, at 03:01 PM EST